‘I am a person with a love for numbers but haven’t graduated with a degree in statistics or maths. Can I learn data science?’
‘I am a quantitative problem solver but don’t have a master’s degree. Am I eligible to become a data scientist?’
‘I am good at writing excel macros and have an affinity towards statistical analysis. Can I pursue my career as a data scientist?’
‘I have good programming knowledge and know a bit of mathematical modeling. Can I become a data scientist?’
These are a few of the many questions, that career counselors at ProjectPro, get asked while counseling professionals on data science careers. If you are a recent college graduate with great interest in quantitative/statistical analysis and you do programming occasionally – it is likely that you have come across the phrase – “Data Science is the hottest career of the century” and this has no doubt interested you to get started, on the data science career path. Leading data science experts from ProjectPro answer the question- “What are the Prerequisites to learn Data Science?” If you are looking to get your foot through the professional data science door, then do read the article completely to decide if data science is the best career move for you.
Kirk Borne, a data scientist with Booz Allen Hamilton said – “There are many skills under the umbrella of data science, and we should not expect anyone single person to be a master of them all. So I suggest that you become an expert in two or more skill areas, but also have a working knowledge of the others.”
You might have come across several online resources which state that becoming a data scientist requires a candidate to possess expert skills in various fields like software development, database query languages, machine learning, programming, mathematics, statistics, data visualization, etc. This seems like a lot – and many do get discouraged once they go through this immense list of skills that they are told is necessary to become a data scientist. This, however, is not the case – as many senior data scientists, who teach at ProjectPro say-One need not possess a lifetime worth of data scientist skills to start learning data science because “Data Scientist” is like a blanket job title where each one is of a different hue and share similar conceptual models and philosophies. There are different types of data science jobs one can apply for, by understanding the data science job descriptions clearly. Data Scientist skills are so varied, that it needs to be understood as to which skills one already possesses to become a data scientist and which ones can be developed over time to match the open data science jobs. However, there are certain prerequisites to fulfill before one can begin their data scientist training -
A master’s data science degree program or a Ph.D. might be a way to go, in developing and waving a technical data science skillset to potential employers but is not a prerequisite to get started with a career in data science. Lack of a highly quantitative degree does not debar one from learning data science. It is possible to learn data science even without a Master’s degree. For high-functioning individuals, who really have the knowledge and expertise with the required tech skills, having a Master’s or a Ph.D. does not matter in the data science space. Real data science experience always outweighs the time spent in acquiring a Master’s degree or a Ph.D. because getting a Ph.D. can prove to be a very long grind.
A master’s or a Ph.D. is definitely a plus when you are applying for a job but not having a Ph.D. is not going to stop you from becoming a data scientist. If you are applying for a data science job at Google, then a master’s or a Ph.D. might be a requirement but other companies will have biases in other directions for hiring a data scientist. PhDs matter only if you are applying for a higher-level or a senior data science position. When beginning to learn data science, Ph.D. or a Master’s Degree is not a necessity.
Data science teams have people from diverse backgrounds like chemical engineering, physics, economics, statistics, mathematics, operations research, computer science, etc. You will find many data scientists with a bachelor’s degree in statistics and machine learning but it is not a requirement to learn data science. However, having familiarity with the basic concepts of Math and Statistics like Linear Algebra, Calculus, Probability, etc. is important to learn data science. Larry Wasserman's All of Statistics: A Concise Course in Statistical Inference is a must-read book for people who want to get a solid background in Statistics.
Programming is an essential skill to become a data scientist but one need not be a hard-core programmer to learn data science. Having familiarity with basic concepts of object-oriented programming like C, C++ or Java will ease the process of learning data science programming tools like Python and R. These basic concepts of programming should help a candidate get a long way on the journey to pursue a career in data science as data science is all about writing efficient code to analyze big data and not being a master of programming. ProjectPro offers introductory sample data science and machine learning code examples where individuals can learn the basics of programming in Python before they begin to learn data science in Python through hands-on projects.
Most of the data scientist’s time is spent in writing SQL and related scripts. Knowing how to write a basic SQL query and having familiarity with joins, group by, having, creating indexes, etc. is important to learn the art of data science. One need not be a database administrator to become a data scientist but unless you have basic SQL knowledge you cannot get the data out, for analysis. Regardless of whether the data is to be retrieved from a database or a Hadoop cluster, there is an SQL language layer always present on the top.
There are many technologies that are emerging for SQL interfacing with Hadoop so for a data scientist to know how to write a Hadoop MapReduce job is not necessary. Knowledge of basic distributed system concepts like MapReduce, Pig, Hive would be helpful but again it depends on which company you will be working for. Many companies have started using Hadoop-as-a-Service so data scientists need not have an in-depth working knowledge of Hadoop.
Neither a data science degree program nor a data science online course (MOOC) provides real training on the end-to-end lifecycle of data science projects. So, a better alternative to learning data science is to work on diverse hands-on data science and machine learning projects that help learn data science without having to spend big money and time on university degree programs. There is a large practical gap between online courses and real industry projects in Machine Learning and Big Data. Real industry projects have complex datasets, cutting-edge techniques, visualization, deployment, and business insights. ProjectPro helps you gain expertise in data science and machine learning through a library of 70+ solved, end-to-end data science and machine learning projects.
Machine learning is an integral part of data science but to begin a career in data science it is not necessary to know the machine learning concepts in advance. Because, if you already know machine learning then you are halfway through your data science career. There are many MOOC providers that offer data science courses in Python and R which cover all the required theoretical concepts of machine learning but the best way to learn data science is to work on diverse hands-on projects and get exposure to a broad range of machine learning concepts. However, if you are still keen on learning machine learning concepts then ProjectPro industry experts suggest a few must-read books-
Here’s a simple checklist for you to assess if you have all the pre-requisites to learn data science and become an enterprise data scientist-
If you have answered YES to all the above, then you can Enrol Now with ProjectPro to learn Data Science. However, if you have answered NO to any of the above statements then please do get in touch with career counselors at ProjectPro for career advice and guidance on the learning path.
You have manipulated, organized, and mined large datasets but you seem to lack specific technical skills like Python, R, Hadoop, NoSQL, Machine Learning - to become a data scientist which most of the data science job postings are asking for. Considering how quickly the data science domain is ramping up, candidates might be better off learning from industry experts who are practicing data science anyway. Python and R are the necessary technical skills that can help one to become a live data scientist (one who automates most of the data science processes to produce desired output) instead of just being a static data scientist who works in a manual fashion with data. Have got all the pre-requisites to learn data science along with a curiosity for data then what are you waiting for get started working on diverse hands-on data science projects to put your skills to practice! Having taken ProjectPro’s subscription, candidates can continue learning data science on the job and make sure that they do not miss out on any opportunity to get involved in the details of the projects their team is working on and also stay updated on the latest tools and technologies.